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Online learning framework for V2V link quality prediction

: Ramya Panthangi, M.; Boban, M.; Zhou, C.; Stanczak, S.


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE Global Communications Conference, GLOBECOM 2019. Proceedings : 9th - 13th December 2019, Waikoloa, HI, USA
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-0962-6
ISBN: 978-1-7281-0963-3
Global Communications Conference (GLOBECOM) <2019, Waikoloa/Hawaii>
Fraunhofer HHI ()

To meet the Quality-of-Service (QoS) requirements of vehicular applications, some knowledge of future wireless channel statistics is essential. We address the problem of predicting channel quality between vehicles in terms of path loss which, exhibits strong fluctuations over time due to highly dynamic vehicular environment. We propose a framework for data-driven path loss prediction models that are obtained from datasets comprising information related to message transmissions and the communication scenario. By combining changepoint detection method and online learning, the proposed framework adapts the current prediction model based on its performance, thus accounting for the dynamics in the environment and the cost of re-training. Evaluations using real world Vehicle-to-Vehicle communications datasets show that adapting the prediction function using the proposed framework can achieve prediction accuracy comparable to that of online learning case, while significantly reducing the number of data samples required for re-training.